Treelines are sensitive indicators of climate warming, with vegetation greenness serving as a vital proxy for physiological responses. However, capturing the fine-scale temporal dynamics of this greenness remains challenging because traditional remote sensing observations are temporally sparse. Here, we leverage near-daily seamless data cube (spatiotemporal fusion of MODIS and Landsat-based datasets, 2000–2021) to eliminate seasonal sampling biases and quantify the distribution and greening of the treeline across the Changbai Mountains at unprecedented spatiotemporal detail. Our analysis reveals that alpine treelines have the mean elevation of 1841 ± 119 m and exhibit disproportionately rapid greening responses, with treeline experienced significantly faster greening rates (2.5 × 10−3 yr−1, derived from normalized difference vegetation index) compared to lower elevation developed forests (1.8–2.1 × 10−3 yr−1), with greening rates declining by 4.5 × 10−4 yr−1 per 100 m along the elevation gradient. Attribution analysis identifies that rising temperature is the dominant driver of this fast treeline greening, with drought, precipitation, and anthropogenic disturbances acting as significant secondary modulators. These findings highlight that vegetation greenness serves as a more immediate and sensitive indicator of warming than the slower demographic process of treeline migration and underscore the value of near-daily Earth observations for monitoring high-mountain vegetation.
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Zhen Xu
Siyu Li
Yanbian University
Xiaoyi Wang
Chinese Academy of Sciences
SHILAP Revista de lepidopterología
Geo-spatial Information Science
Chinese Academy of Sciences
University of Hong Kong
Peng Cheng Laboratory
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Xu et al. (Mon,) studied this question.
synapsesocial.com/papers/69e1cd6f5cdc762e9d856e7d — DOI: https://doi.org/10.1080/10095020.2026.2648348